{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T23:29:16Z","timestamp":1777418956778,"version":"3.51.4"},"reference-count":13,"publisher":"SPIE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,3,2]]},"DOI":"10.1117\/12.2293528","type":"proceedings-article","created":{"date-parts":[[2018,3,2]],"date-time":"2018-03-02T21:42:47Z","timestamp":1520026967000},"page":"29","source":"Crossref","is-referenced-by-count":16,"title":["Foveal fully convolutional nets for multi-organ segmentation"],"prefix":"10.1117","author":[{"given":"Tom","family":"Brosch","sequence":"first","affiliation":[]},{"given":"Axel","family":"Saalbach","sequence":"first","affiliation":[]}],"member":"189","reference":[{"key":"c1","first-page":"2843","article-title":"Deep neural networks segment neuronal membranes in electron microscopy images","author":"Cire\u015fan","year":"2012"},{"key":"c2","first-page":"20","article-title":"Deep neural networks for anatomical brain segmentation","author":"de Brebisson","year":"2015"},{"key":"c3","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"key":"c4","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528821"},{"key":"c5","unstructured":"Ronneberger, O., Fischer, P., and Brox, T., \u201cU-net: Convolutional networks for biomedical image segmentation,\u201d in [MICCAI 2015, Part III], LNCS 9351, 234\u2013241, Springer (2015)."},{"key":"c6","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.10.004"},{"key":"c7","first-page":"424","article-title":"3D U-Net: learning dense volumetric segmentation from sparse annotation","author":"\u00c7i\u00e7ek","year":"2016"},{"key":"c8","doi-asserted-by":"publisher","DOI":"10.2307\/1932409"},{"key":"c9","first-page":"2980","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017"},{"key":"c10","first-page":"1212.5701","article-title":"ADADELTA: an adaptive learning rate method","volume":"arXiv","author":"Zeiler","year":"2012"},{"key":"c11","first-page":"92","article-title":"Visceral: Towards large data in medical imaging-challenges and directions","author":"Langs","year":"2012"},{"key":"c12","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2578680"},{"key":"c13","first-page":"598","article-title":"Multi-organ segmentation using vantage point forests and binary context features","volume":"9901","author":"Heinrich","year":"2016"}],"event":{"name":"Image Processing","location":"Houston, United States","start":{"date-parts":[[2018,2,10]]},"end":{"date-parts":[[2018,2,15]]}},"container-title":["Medical Imaging 2018: Image Processing"],"original-title":[],"deposited":{"date-parts":[[2018,5,23]],"date-time":"2018-05-23T22:09:13Z","timestamp":1527113353000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/10574\/2293528\/Foveal-fully-convolutional-nets-for-multi-organ-segmentation\/10.1117\/12.2293528.full"}},"subtitle":[],"editor":[{"given":"Elsa D.","family":"Angelini","sequence":"first","affiliation":[]},{"given":"Bennett A.","family":"Landman","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2018,3,2]]},"references-count":13,"URL":"https:\/\/doi.org\/10.1117\/12.2293528","relation":{},"subject":[],"published":{"date-parts":[[2018,3,2]]}}}